title: Differentiation between glioblastomas and solitary brain metastases using diffusion tensor imaging.
abstract: The purpose of this study is to determine whether diffusion tensor imaging (DTI) metrics including tensor shape measures such as linear and planar anisotropy coefficients (CL and CP) can help differentiate glioblastomas from solitary brain metastases. Sixty-three patients with histopathologic diagnosis of glioblastomas (22 men, 16 women, mean age 58.4 years) and brain metastases (13 men, 12 women, mean age 56.3 years) were included in this study. Contrast-enhanced T1-weighted, fluid-attenuated inversion recovery (FLAIR) images, fractional anisotropy (FA), apparent diffusion coefficient (ADC), CL and CP maps were co-registered and each lesion was semi-automatically subdivided into four regions: central, enhancing, immediate peritumoral and distant peritumoral. DTI metrics as well as the normalized signal intensity from the contrast-enhanced T1-weighted images were measured from each region. Univariate and multivariate logistic regression analyses were employed to determine the best model for classification. The results demonstrated that FA, CL and CP from glioblastomas were significantly higher than those of brain metastases from all segmented regions (p<0.05), and the differences from the enhancing regions were most significant (p<0.001). FA and CL from the enhancing region had the highest prediction accuracy when used alone with an area under the curve of 0.90. The best logistic regression model included three parameters (ADC, FA and CP) from the enhancing part, resulting in 92% sensitivity, 100% specificity and area under the curve of 0.98. We conclude that DTI metrics, used individually or combined, have a potential as a non-invasive measure to differentiate glioblastomas from metastases.
authors.affiliation: Department of Radiology, Division of Neuroradiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Sumei
authors.initials: S
authors.lastname: Wang
authors.forename: Sungheon
authors.initials: S
authors.lastname: Kim
authors.forename: Sanjeev
authors.initials: S
authors.lastname: Chawla
authors.forename: Ronald L
authors.initials: RL
authors.lastname: Wolf
authors.forename: Wei-Guo
authors.initials: WG
authors.lastname: Zhang
authors.forename: Donald M
authors.initials: DM
authors.lastname: O'Rourke
authors.forename: Kevin D
authors.initials: KD
authors.lastname: Judy
authors.forename: Elias R
authors.initials: ER
authors.lastname: Melhem
authors.forename: Harish
authors.initials: H
authors.lastname: Poptani
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D001932:Brain Neoplasms / Q000473:pathology* / Q000556:secondary*; D016000:Cluster Analysis; D003937:Diagnosis, Differential; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D005909:Glioblastoma / Q000473:pathology* / Q000556:secondary*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity